Genotype X Environment Interactions and Its Impact on Use of Medicinal Plants
Bibliographic record
Abstract
There is a paradigm shift from cure to prevention when it comes to human health.We want to live a healthy life and prevent sickness using substances other than pharmaceuticals.The plant-based nutraceutical products or Natural Health Products (NHPs) as they are some times referred to are the most important groups that have the potential to fit the bill.However, these products are sold without proper science based information in spite of the fact that most researchers acknowledge the need for such information.Evidence-based scientific studies to support health and nutraceutical claims related to the use of medicinal plants and their extracts have to be undertaken.It is only through critical research efforts that we can provide strong endorsements for medicinal plant use and ensure consumer confidence in the industry.Much of the research published on the medicinal value of plants does not take into account variability generated from genetic differences among plants and their interaction with the environment.Research should be directed towards properly identifying plants with known medicinal properties which have been grown in environments that are conducive to consistent production of the active agents attributed to the plants.Production of dependable medicinal plant products can only be attained if we pay close attention to these research-based principles.This article was written with main goals: 1) To discuss the above points in greater detail with examples; and 2) to highlight life time accomplishments and significant contributions of a well respected nutritionist Dr. T. K. Basu and his collaboration in development of fenugreek as a NHP.We believe that collaboration among clinical and agricultural researchers is essential to make the NHPs utilized to its potential and the plants (parts such as seed, foliage or roots) should be developed to the extent that they can be used directly to take advantage of the synergistic effect of the chemical constituents.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".